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alexgshaw
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hyperpartisan-classifier

Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - hyperpartisan_news_detection
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: hyperpartisan-classifier
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+ results:
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+ - task:
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+ name: Text Classification
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+ type: text-classification
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+ dataset:
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+ name: hyperpartisan_news_detection
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+ type: hyperpartisan_news_detection
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+ config: bypublisher
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+ split: validation
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+ args: bypublisher
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9988466666666667
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # hyperpartisan-classifier
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+
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+ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the hyperpartisan_news_detection dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0036
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+ - Accuracy: 0.9988
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.1441 | 0.11 | 1000 | 0.1391 | 0.9453 |
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+ | 0.1248 | 0.21 | 2000 | 0.1042 | 0.9595 |
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+ | 0.1027 | 0.32 | 3000 | 0.0913 | 0.9647 |
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+ | 0.0928 | 0.43 | 4000 | 0.0827 | 0.9688 |
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+ | 0.0992 | 0.53 | 5000 | 0.0799 | 0.9698 |
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+ | 0.0881 | 0.64 | 6000 | 0.0710 | 0.9741 |
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+ | 0.078 | 0.75 | 7000 | 0.0640 | 0.9762 |
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+ | 0.0708 | 0.85 | 8000 | 0.0626 | 0.9764 |
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+ | 0.0696 | 0.96 | 9000 | 0.0564 | 0.9792 |
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+ | 0.0586 | 1.07 | 10000 | 0.0516 | 0.9813 |
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+ | 0.0558 | 1.17 | 11000 | 0.0507 | 0.9815 |
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+ | 0.0531 | 1.28 | 12000 | 0.0463 | 0.9829 |
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+ | 0.0585 | 1.39 | 13000 | 0.0468 | 0.9831 |
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+ | 0.0488 | 1.49 | 14000 | 0.0403 | 0.9854 |
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+ | 0.057 | 1.6 | 15000 | 0.0393 | 0.9865 |
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+ | 0.0514 | 1.71 | 16000 | 0.0349 | 0.9879 |
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+ | 0.052 | 1.81 | 17000 | 0.0366 | 0.9868 |
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+ | 0.0572 | 1.92 | 18000 | 0.0300 | 0.9895 |
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+ | 0.0311 | 2.03 | 19000 | 0.0309 | 0.9893 |
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+ | 0.0332 | 2.13 | 20000 | 0.0262 | 0.9908 |
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+ | 0.0396 | 2.24 | 21000 | 0.0250 | 0.9914 |
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+ | 0.0314 | 2.35 | 22000 | 0.0223 | 0.9924 |
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+ | 0.0361 | 2.45 | 23000 | 0.0236 | 0.9919 |
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+ | 0.0289 | 2.56 | 24000 | 0.0197 | 0.9933 |
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+ | 0.0322 | 2.67 | 25000 | 0.0182 | 0.9939 |
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+ | 0.0416 | 2.77 | 26000 | 0.0183 | 0.9937 |
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+ | 0.0273 | 2.88 | 27000 | 0.0159 | 0.9946 |
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+ | 0.0317 | 2.99 | 28000 | 0.0152 | 0.9949 |
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+ | 0.0203 | 3.09 | 29000 | 0.0132 | 0.9957 |
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+ | 0.0182 | 3.2 | 30000 | 0.0146 | 0.9953 |
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+ | 0.0165 | 3.31 | 31000 | 0.0123 | 0.9961 |
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+ | 0.0184 | 3.41 | 32000 | 0.0105 | 0.9968 |
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+ | 0.0208 | 3.52 | 33000 | 0.0103 | 0.9967 |
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+ | 0.0187 | 3.63 | 34000 | 0.0083 | 0.9973 |
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+ | 0.0183 | 3.73 | 35000 | 0.0076 | 0.9977 |
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+ | 0.0258 | 3.84 | 36000 | 0.0073 | 0.9977 |
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+ | 0.0114 | 3.95 | 37000 | 0.0066 | 0.9979 |
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+ | 0.007 | 4.05 | 38000 | 0.0052 | 0.9983 |
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+ | 0.0094 | 4.16 | 39000 | 0.0061 | 0.9981 |
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+ | 0.0106 | 4.27 | 40000 | 0.0053 | 0.9983 |
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+ | 0.0134 | 4.37 | 41000 | 0.0052 | 0.9984 |
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+ | 0.0087 | 4.48 | 42000 | 0.0040 | 0.9987 |
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+ | 0.018 | 4.59 | 43000 | 0.0047 | 0.9985 |
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+ | 0.0118 | 4.69 | 44000 | 0.0041 | 0.9987 |
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+ | 0.012 | 4.8 | 45000 | 0.0038 | 0.9988 |
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+ | 0.0165 | 4.91 | 46000 | 0.0036 | 0.9988 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.27.0.dev0
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.9.0
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+ - Tokenizers 0.13.2